4 papers
Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data
Stephen Asiedu, David Watson
Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this ordering provides a natural scaffo…
Probably Approximately Correct Maximum A Posteriori Inference
Matthew Shorvon, Frederik Mallmann-Trenn, David S. Watson
Computing the conditional mode of a distribution, better known as the maximum a posteriori (MAP) assignment, is a fundamental task in probabilistic inference. However, MAP is gener…
Autoencoding Random Forests
Binh Duc Vu, Jan Kapar, Marvin Wright +1
We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a…
Multi-omic Causal Discovery using Genotypes and Gene Expression
Stephen Asiedu, David Watson
Causal discovery in multi-omic datasets is crucial for understanding the bigger picture of gene regulatory mechanisms, but remains challenging due to high dimensionality, different…